I reviewed the manuscript “Hydrologic Model Parameter Estimation in Snow-Dominated Headwater Catchments Using Multiple Observation Datasets” by North et al. This study investigates the value of integrating diverse observational datasets (streamflow, satellite-derived snow, soil moisture, and evapotranspiration) for parameter sensitivity and estimation in the pywatershed hydrological model across four snow-dominated headwater catchments. Using Morris screening and Monte Carlo filtering, we find that while alternative observations consistently identify more informative parameters than streamflow alone, their impact on final streamflow performance is highly catchment-specific. Only actual evapotranspiration (AET) data reliably improved simulations, whereas snow and soil moisture datasets yielded inconsistent results, sometimes degrading performance. The work underscores the context-dependent utility of multi-observational calibration, highlighting challenges such as equifinality, spatial representativeness, and model-observation alignment that must be addressed to effectively leverage new data sources in hydrological forecasting.

This is a timely, well-executed, and intellectually rigorous study that makes a valuable contribution to the field of hydrological model calibration. The experimental design is robust, leveraging state-of-the-art sensitivity and uncertainty analysis techniques (Morris method, LHS Monte Carlo filtering) applied to a relevant and modern modeling code (pywatershed). The manuscript is clearly written, and the analysis convincingly demonstrates both the potential and the pitfalls of integrating diverse, often spatially mismatched, observational datasets. The conclusions are supported by the data presented. I only have a few specific comments in the annotated manuscript file for further clarify the narrative and implications of the work. I would recommend publication after minor revisions.

This manuscript presents an interesting and timely evaluation of how multi-source observational datasets can be used to constrain the pywatershed in snow-dominated headwater catchments. The integration of diverse data products (such as ASO SWE and OpenET) to address the pervasive issue of model equifinality is a significant undertaking that holds substantial value for the hydrological community. The study's focus on identifying which observations provide truly informative vs. potentially misinformative constraints is particularly important for advancing operational modeling practices. However, while the research is promising, there are several points that warrant further discussion and clarification before the paper can be considered for publication. My comments are attached.